|Marcel van Gerven
I am interested in the computational principles that underly adaptive behaviour. The questions that I focus on are how the brain is able to extract information from its environment and use this information in order to generate optimal actions. My main goal is to develop biologically plausible neural network models that further our understanding of natural intelligence and provide a route towards solving the strong AI problem.
I use computational models on (high-field) fMRI and MEG to investigate the neural mechanisms underlying perception and memory. Specifically, I am interested in how these bottom-up and top-down processes are implemented and interact in the human brain.
I am working on my Veni project together with with Marcel van Gerven, Pieter Medendorp and Roy Kessels. My work focuses on the relation between functional and structural changes in the aging brain and their joint association with cognition.
I am interested in structural and functional brain connectivity. In particular I study different (probabilistic) generative models and develop techniques efficiently compute them. Two central themes in my research are integration of different imaging modalities (e.g. fMRI and dMRI) and explicit modeling of uncertainty in connectivity estimates.
I have a strong interest in developing methods through which connectivity estimates derived from multiple modalities can be compared and integrated. My present focus involves combining MRI and MEG evidence to evaluate how neuromodulatory systems interact with hippocampal and cortical circuits to produce cognition, and how this may break down in neurodegenerative diseases such as Alzheimer’s.
I am mainly interested in visual experience in the absence of visual input. During my PhD I will investigate to what extent visual imagery relies on the same neural mechanisms as visual perception. Besides neuroscience, I also have a strong interest in philosophy of mind and consciousness.
My primary research interest is in developing computational models of (ultra-high-field) fMRI and MEG data to characterize the relationship between cognitive processes and brain connectivity. Besides brain connectivity, I am also interested in neural coding, unsupervised feature learning and deep learning.
I am interested in the architecture of brain connectivity and its relation to health and disease. To look at this I use Bayesian methods to combine information from resting state functional and diffusion-weighted MRI.
My research interest lies in audiovisual integration, e.g. the involvement and interplay of primary sensory and higher order areas. I investigate this using fMRI and multivariate pattern analysis. A phenomenon which I make use of is the so called McGurk effect (simultaneous visual ‘aga’ and auditory ‘aba’ is fused and perceived as ‘ada’).
How does the brain extract complex features and concepts from the information entering our senses? I am particularly interested in how individual neurons, forming a complex network, can perform this task. I apply computational models to experimental data of high temporal resolution to unravel the dynamics of the mechanisms involved in this process.
I apply deep neural networks to affective computing for use in robotics, focusing on methods for the interpretability of black-box machine learning algorithms and aiming to improve human-robot interaction.
I apply machine learning techniques to fMRI data to reconstruct low-level and high-level properties of perceived images.
I am focusing on encoding and decoding models. One major topic is identifying biologically plausible feature transformations by investigating to what extent deep learning can predict human perceptual processing. I also work on the optimisation of this mapping and its inversion using statistical machine learning techniques.
Visual sensory information, as we receive it on our retina, mainly contains partially hidden objects. Instead of perceiving them as fragmented, we perceive them as completed objects. I am interested in how the brain achieves this amodal completion, and how it (and its underlying neural mechanism) is related to other phenomena such as modal completion and imagery.
- Michele D’Asaro (supervisors: Silvan, Marcel)
- Hugo Dictus (supervisors: Katja, Silvan, Marcel)
- Patrick Ebel (supervisors: Max)
- Wieke Kanters (supervisors: Max, Marcel)
- Filippos Panagiotou (supervisors: Sander Marcel)
- Josh Ring (supervisors: Marcel)
- Marjolein Troost (supervisors: Katja, Marcel)
- Inez Wijnands (supervisors: Ronald, Max, Marcel)
- Marcel Zuur (supervisors: Silvan, Marcel)
- Erdi Çalli
- Marieke van de Nieuwenhuijzen (became researcher at UMC Utrecht)
- Ali Bahramisharif (became an assistant professor at the University of Amsterdam)
- Haiteng Jiang (became a postdoc at the University of Minnesota)
- Elena Shumskaya (became a postdoc at the Donders Centre for Cognitive Neuroimaging)
- Irina Simanova (became a postdoc at the Donders Centre for Cognition)
- Pasi Jylänki (works for a London-based investment fund)
Former master students
- Judith Rudolph, 2017, The neural representations of simple and complex movement sequences
- Rowan Sommers, 2017, Neural encoding of densely sampled fMRI voxel responses to naturalistic audio-visual stimuli
- Kevin Koschmieder, 2016, Representational similarity analysis of haemodynamic responses during ATARI video gameplay using deep Q network feature regressors
- Jaap Buurman, 2016, Utilising FORCE learning to model adaptive behaviour
- Edward Grant, 2016, Deep disentangled representations for volumetric reconstruction
- Steffen Kaiser, 2016, Decoding spatial representations from functional magnetic resonance imaging data; Dutch MSc Best Thesis Award for Cognitive Neurosciences 2015-2016
- Jordy Thielen, 2016, Deep learning to probe neural correlates of music processing
- Farhad Ghazvinian, 2016, Improving semantic video segmentation by dynamic scene integration
- Tom van Koppen, 2016, Bayesian population receptive field models
- Jenya Bednaya, 2015, Decoding of concepts within and across semantic categories
- Lonneke Teunissen, 2015, Bayesian integration of tactile and motor information reduces uncertainty about an object’s location in mice
- Annet Meijers, 2015, Predicting connectomes using noisy and incomplete data
- Nadine Dijkstra, 2015, The spatiotemporal dynamics of binocular rivalry: Evidence for increased top-down flow prior to a perceptual switch
- Yuliya Berezutskaya, 2014, The representation of mammals in the human brain
- Luca Ambrogioni, 2014, The dynamic role of alpha-band brain oscillations during audiovisual integration of natural scenes
- Umut Güçlü, 2013, Unsupervised learning of linearizing feature spaces for encoding and decoding in functional magnetic resonance imaging; Radboud University Study Prize
- Louis Onrust, 2013, Bayesian inference of structural brain networks with region-specific Dirichlet parametrisation
- Arvind Datadien, 2013, A biologically plausible reservoir computing model with delay learning
- Reinout Versteeg, 2013, Methods to identify semantic content differences
- Tijn Schouten, 2013, Gender classification from functional brain connectivity
- Adnan Niazi, 2012, Real-time fMRI decoding: reading minds using brain imaging
- Alexander Backus, 2011, Investigating the temporal dynamics of long‐term memory representation retrieval using multivariate pattern analyses on magnetoencephalography data
- Benjamin Mader, 2011, Localization and mapping with autonomous robots
- Martin Laverman, 2009, The preprocessing of fMRI data for use in a classifier-based analysis